@InProceedings{Kumar2012,
    author = {Neeraj Kumar and Peter N. Belhumeur and Arijit Biswas and David W. Jacobs and W. John Kress and Ida Lopez and João V. B. Soares},
    title = {Leafsnap: A Computer Vision System for Automatic Plant Species Identification},
    organization = {Springer Berlin Heidelberg},
    booktitle = {The 12th European Conference on Computer Vision (ECCV)},
    month = {October},
    year = {2012}
}
@article{Oliva2001,
abstract = {In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimen- sional representation of the scene, that we term the Spatial Envelope. We propose a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space in which scenes sharing membership in semantic categories (e.g., streets, highways, coasts) are projected closed together. The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.},
author = {Oliva, Aude and Torralba, Antonio},
doi = {10.1023/A:1011139631724},
file = {:Users/Jenny/Documents/PhD/Mendeley/Oliva, Torralba\_2001\_Modeling the shape of the scene A holistic representation of the spatial envelope.pdf:pdf},
isbn = {0920-5691},
issn = {09205691},
journal = {International Journal of Computer Vision},
keywords = {Energy spectrum,Natural images,Principal components,Scene recognition,Spatial layout},
number = {3},
pages = {145--175},
title = {{Modeling the shape of the scene: A holistic representation of the spatial envelope}},
volume = {42},
year = {2001}
}
@article{Agarwal2006,
abstract = {We describe an ongoing project to digitize information about plant specimens and make it available to botanists  in the field. This first requires digital images and models, and then effective retrieval and mobile computing  mechanisms for accessing this information. We have almost completed a digital archive of the collection of  type specimens at the Smithsonian Institution Department of Botany. Using these and additional images, we  have also constructed prototype electronic field guides for the flora of Plummers Island. Our guides use a novel  computer vision algorithm to compute leaf similarity. This algorithm is integrated into image browsers that  assist a user in navigating a large collection of images to identify the species of a new specimen. For example,  our systems allow a user to photograph a leaf and use this image to retrieve a set of leaves with similar shapes.  We measured the effectiveness of one of these systems with recognition experiments on a large dataset of  images, and with user studies of the complete retrieval system. In addition, we describe future directions for  acquiring models of more complex, 3D specimens, and for using new methods in wearable computing to inter-  act with data in the 3D environment in which it is acquired.  },
author = {Agarwal, Gaurav and Belhumeur, Peter and Feiner, Steven and Jacobs, David and Kress, W. John and Ramamoorthi, Ravi and Bourg, Norman a. and Dixit, Nandan and Ling, Haibin and Mahajan, Dhruv and Russell, Rusty and Shirdhonkar, Sameer and Sunkavalli, Kalyan and White, Sean},
doi = {10.2307/25065637},
file = {:Users/Jenny/Documents/PhD/Mendeley/Agarwal et al.\_2006\_First steps toward an electronic field guide for plants.pdf:pdf},
isbn = {0040-0262},
issn = {00400262},
journal = {Taxon},
keywords = {Augmented reality,Computer vision,Content-based image retrieval,Electronic field guide,Inner-distance,Mobile computing,Recognition,Shape matching,Species identification,Type specimens,Wearable computing},
number = {3},
pages = {597--610},
title = {{First steps toward an electronic field guide for plants}},
volume = {55},
year = {2006}
}
@article{Belhumeur2008a,
abstract = {We describe a working computer vision System that aids in the identification of plant species. A user photographs an isolated leaf on a blank background, and the system extracts the leaf shape and matches it to the shape of leaves of known species. In a few seconds, the system displays the top matching species, along with textual descriptions and additional images. This system is currently in use by botanists at the Smithsonian Institution National Museum of Natural History. The primary contributions of this paper are: a description of a working computer vision system and its user interface for an important new application area; the introduction of three new datasets containing thousands of single leaf images, each labeled by species and verified by botanists at the US National Herbarium; recognition results for two of the three leaf datasets: and descriptions throughout of practical lessons learned in constructing this system.},
author = {Belhumeur, Peter N. and Chen, Daozheng and Feiner, Steven and Jacobs, David W. and Kress, W. John and Ling, Haibin and Lopez, Ida and Ramamoorthi, Ravi and Sheorey, Sameer and White, Sean and Zhang, Ling},
doi = {10.1007/978-3-540-88693-8-9},
file = {:Users/Jenny/Documents/PhD/Mendeley/Belhumeur et al.\_2008\_Searching the world's Herbaria A system for visual identification of plant species.pdf:pdf},
isbn = {3540886923},
issn = {03029743},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {116--129},
pmid = {19144595},
title = {{Searching the world's Herbaria: A system for visual identification of plant species}},
volume = {5305 LNCS},
year = {2008}
}
@article{Nilsback2008,
abstract = {We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower dataset. We compute four different features for the flowers, each describing different aspects, namely the local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour. We combine the features using a multiple kernel framework with a SVM classifier. The weights for each class are learnt using the method of Varma and Ray, which has achieved state of the art performance on other large dataset, such as Caltech 101/256. Our dataset has a similar challenge in the number of classes, but with the added difficulty of large between class similarity and small within class similarity. Results show that learning the optimum kernel combination of multiple features vastly improves the performance, from 55.1\% for the best single feature to 72.8\% for the combination of all features.},
author = {Nilsback, Maria Elena and Zisserman, Andrew},
doi = {10.1109/ICVGIP.2008.47},
file = {:Users/Jenny/Documents/PhD/Mendeley/Nilsback, Zisserman\_2008\_Automated flower classification over a large number of classes.pdf:pdf},
isbn = {9780769534763},
issn = {<null>},
journal = {Proceedings - 6th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2008},
pages = {722--729},
title = {{Automated flower classification over a large number of classes}},
year = {2008}
}
@article{Cerutti2011a,
abstract = {In this paper we present a system for tree leaf segmentation in natural images that combines a first, unrefined segmentation step, with an estimation of descriptors depicting the general shape of a simple leaf. It is based on a light polygonal model, built to represent most of the leaf shapes, that will be deformed to fit the leaf in the image. Avoiding some classic obstacles of active contour models, this approach gives promising results, even on complex natural photographs, and constitutes a solid basis for a leaf recognition process.},
author = {Cerutti, Guillaume and Tougne, Laure and Vacavant, Antoine and Coquin, Didier},
doi = {10.1007/978-3-642-24028-7\_19},
file = {:Users/Jenny/Documents/PhD/Mendeley/Cerutti et al.\_2011\_A parametric active polygon for leaf segmentation and shape estimation.pdf:pdf},
isbn = {9783642240270},
issn = {03029743},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {202--213},
title = {{A parametric active polygon for leaf segmentation and shape estimation}},
volume = {6938 LNCS},
year = {2011}
}
@article{Valliammal2012b,
abstract = {This paper presents a new approach for plant leaf image segmentation by applying non linear k means algorithm. The segmentation process presents a clustering mechanism for high resolution images in order to improve the precision and processing time. Plant image, however, always contain complicated background objects that interfere with the examination process and must be removed from the image prior to species classification. K means clustering is applied at the first level of segmentation to detect the structure of the plant leaf. At the second level Sobel edge detector is used to remove the unwanted segments to extract the exact part of the leaf shape. The performance of the proposed method is compared with other traditional methods to analyze the efficiency of the system. Experimental result shows that this new approach simplifies the process to extract shape related features and measurements of the leaf for higher accuracy.},
author = {Valliammal},
file = {:Users/Jenny/Documents/PhD/Mendeley/Valliammal\_2012\_Plant Leaf Segmentation Using Non Linear K means Clustering.pdf:pdf},
journal = {International Journal of Computer Science Issues},
keywords = {applications,clustering,edge detection,important botanical skill,k means,of plants is an,plant leaf identification,ranging from plant recognition,sobel edge detector,to health,with many},
number = {3},
pages = {212--218},
title = {{Plant Leaf Segmentation Using Non Linear K means Clustering}},
volume = {9},
year = {2012}
}
@article{Teng2009a,
abstract = {In this paper, we present a complete system to extract leaves, recover their 3D positions and finally classify them based on leaf shape. We use only a few images with slightly different viewpoints to achieve the task. The images are captured by a general hand-held digital camera and no camera pre-calibration is required. Because only a few images with close viewpoints are sufficient to segment the leaves and recover their 3D positions, our system is flexible and easy to use in image acquisition. For leaf classification, we use the normalized centroid-contour distance as our classification feature and employ a circular-shift comparing scheme to measure the similarity, thus our system has the advantages of being invariant to leaf translation, rotation and scaling. We have conducted several experiments and the results are encouraging. The leaves are nearly perfectly extracted and the classification results are also acceptable. \&copy; 2009 Springer Berlin Heidelberg.},
author = {Teng, Chin Hung and Kuo, Yi Ting and Chen, Yung Sheng},
doi = {10.1007/978-3-642-02611-9\_92},
file = {:Users/Jenny/Documents/PhD/Mendeley/Teng, Kuo, Chen\_2009\_Leaf segmentation, its 3D position estimation and leaf classification from a few images with very close viewpoints.pdf:pdf},
isbn = {3642026109},
issn = {03029743},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Leaf 3D Recovery,Leaf Classification,Leaf Segmentation},
pages = {937--946},
title = {{Leaf segmentation, its 3D position estimation and leaf classification from a few images with very close viewpoints}},
volume = {5627 LNCS},
year = {2009}
}
@article{Branson2010,
abstract = {We present an interactive, hybrid human-computer method for object classification. The method applies to classes of objects that are recognizable by people with appropriate expertise (e.g.,animal species or airplane model), but not (in general) by people without such expertise. It can be seen as a visual version of the 20 questions game,where questions based on simple visual attributes are posed interactively. The goal is to identify the true class while minimizing the number of questions asked, using the visual content of the image. We introduce a general framework for incorporating almost any off-the-shelf multi-class object recognition algorithm into the visual 20 questions game, and provide methodologies to account for imperfect user responses and unreliable computer vision algorithms. We evaluate our methods on Birds-200, a difficult dataset of 200 tightly-related bird species, and on the Animals With Attributes dataset. Our results demonstrate that incorporating user input drives up recognition accuracy to levels that are good enough for practical appli- cations, while at the same time, computer vision reduces the amount of human interaction required.},
author = {Branson, Steve and Wah, Catherine and Schroff, Florian and Babenko, Boris and Welinder, Peter and Perona, Pietro and Belongie, Serge},
doi = {10.1007/978-3-642-15561-1\_32},
file = {:Users/Jenny/Documents/PhD/Mendeley/Branson et al.\_2010\_Visual recognition with humans in the loop.pdf:pdf},
isbn = {364215560X},
issn = {03029743},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {438--451},
title = {{Visual recognition with humans in the loop}},
volume = {6314 LNCS},
year = {2010}
}
@article{fitzgibbon2003robust,
title={Robust registration of 2D and 3D point sets},
author={Fitzgibbon, Andrew W},
journal={Image and Vision Computing},
volume={21},
number={13},
pages={1145--1153},
year={2003},
publisher={Elsevier}
}
@article{Pottmann2009,
abstract = {Differential invariants of curves and surfaces such as curvatures and their derivatives play a central role in Geometry Processing. They are, however, sensitive to noise and minor perturbations and do not exhibit the desired multi-scale behavior. Recently, the relationships between differential invariants and certain integrals over small neighborhoods have been used to define efficiently computable integral invariants which have both a geometric meaning and useful stability properties. This paper considers integral invariants defined via distance functions, and the stability analysis of integral invariants in general. Such invariants proved useful for many tasks where the computation of shape characteristics is important. A prominent and recent example is the automatic reassembling of broken objects based on correspondences between fracture surfaces. © 2008 Elsevier B.V. All rights reserved.},
author = {Pottmann, Helmut and Wallner, Johannes and Huang, Qi Xing and Yang, Yong Liang},
doi = {10.1016/j.cagd.2008.01.002},
file = {:Users/Jenny/Documents/PhD/Mendeley/Pottmann et al.\_2009\_Integral invariants for robust geometry processing.pdf:pdf},
isbn = {0167-8396},
issn = {01678396},
journal = {Computer Aided Geometric Design},
keywords = {3D shape understanding,Curvature,Geometry processing,Integral invariant,Stability},
pages = {37--60},
title = {{Integral invariants for robust geometry processing}},
volume = {26},
year = {2009}
}

